Pinned Repositories
2Dskeleton
code for "2D Skeleton Extraction Based on Heat Equation", Computer&Graphics 2018.
arnheim
awesome-computational-neuroscience
A list of schools and researchers in computational neuroscience
awesome-gan-for-medical-imaging
Awesome GAN for Medical Imaging
Breast-Cancer-Identification
Breast Cancer Identification Based on GLCM Texture Feature of Mammogram Image using Backpropagation Artificial Neural Network and K-Nearest Neighbor (KNN)
BUS-GAN
Semi-supervised Segmentation of Tumors from Breast Ultrasound Images with Attentional Generative Adversarial Network
CNN-BUS-segment
Convolutional neural networks for semantic segmentation of tumors in breast ultrasound images
color_hmax
Hmax color
cs284-image-segmentation
Image Segmentation using Conditional Random Fields
datasciencecoursera
nalankaru's Repositories
nalankaru/arnheim
nalankaru/awesome-computational-neuroscience
A list of schools and researchers in computational neuroscience
nalankaru/BUS-GAN
Semi-supervised Segmentation of Tumors from Breast Ultrasound Images with Attentional Generative Adversarial Network
nalankaru/CNN-BUS-segment
Convolutional neural networks for semantic segmentation of tumors in breast ultrasound images
nalankaru/datasciencecoursera
nalankaru/dataset-uta4-dicom
:bar_chart: [AVI 2020] UTA4: Medical Imaging DICOM Files Dataset
nalankaru/datasharing
The Leek group guide to data sharing
nalankaru/dcm2niix
dcm2nii DICOM to NIfTI converter: compiled versions available from NITRC
nalankaru/DeepLearningInMedicalImagingAndMedicalImageAnalysis
nalankaru/defgrid-release
Official PyTorch implementation of Deformable Grid (ECCV 2020)
nalankaru/dicom2nifti
nalankaru/Dynamics_In_Neuro_Lectures_2021
A set of lectures I gave for Chris Rozell's "Information Processing Models in Neural Systems" course at Georgia Insitute of Technology
nalankaru/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
nalankaru/generative-compression
TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression
nalankaru/image-compression-kmeans
Image compression using K-Means.
nalankaru/ITSRio-TCC
ITSRio TCC (Trabalho de Conclusão de Curso) - Computação
nalankaru/MAgent
A Platform for Many-agent Reinforcement Learning
nalankaru/MedSAM
The official repository for MedSAM: Segment Anything in Medical Images.
nalankaru/ML-Course-Notes
🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.
nalankaru/MOOC-neurons-and-synapses-2017
Reference data for the "Simulation Neuroscience:Neurons and Synapses" Massive Online Open Course
nalankaru/NeuroM
Neuronal Morphology Analysis Tool
nalankaru/NEURONexercises-nalan-bnni
Repository of exercises using the NEURON simulator
nalankaru/NN-SVG
Publication-ready NN-architecture schematics.
nalankaru/nnUNet-Extend
nalankaru/orientation-field-control
Matlab code for orientation field control based on PVFC
nalankaru/pypc
Predictive coding in Python
nalankaru/Slime-Simulation
nalankaru/texSeg
Weakly-Supervised Sparse Coding With Geometric Prior for Interactive Texture Segmentation-SPL 2019 Texture segmentation is about dividing a texturedominant image into multiple homogeneous texture regions. The existing unsupervised approaches for texture segmentation are annotation-free but often yield unsatisfactory results. In contrast, supervised approaches such as deep learning may have better performance but require a large amount of annotated data. In this letter, we propose a user-interactive approach to win the trade-off between unsupervised approaches and supervised deep approaches. Our approach requires the user to mark one pixel in each texture region, whose label is directly propagated to its neighbor region. Such labeled data are of very small amount and even partially erroneous. To effectively exploit such weakly-labeled data, we construct a weakly-supervised sparse coding model that jointly conducts feature learning and segmentation. In addition, the geometric constraints are developed for the model to exploit the geometric prior on the local connectivity of region boundaries. The experiments on two benchmark datasets have validated the effectiveness of the proposed approach.
nalankaru/U-2-Net
The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
nalankaru/US_sample